Cybersecurity Scholarships for UMBC students

Applications sought for major UMBC cybersecurity scholarships

NSF CyberCorps: Scholarship For Service (SFS)

Scholarships for careers in cybersecurity. Earn full tuition, fees, stipends ($22,500 – $34,000), and more ($2000 books, up to $3000 health benefits, $4000 professional expenses).  For BS, MS, MPS, or PhD in CS, CE, IS, Cyber or related fields. USA citizenship or permanent residency required. Contact Dr. Alan Sherman,  who will send you an application.

In academic year 2017-2018, UMBC will support a total of about six additional SFS Scholars at the BS, MS, MPS, and PhD levels in CS and related fields. Each scholarship is potentially for up to the final two years (three years for PhD and combined BS/MS). Interested full-time degree students should contact  and visit the CISA scholarship page.

Each scholarship covers full tuition, fees, travel, books, and academic year stipend of $34,000 for MS/MPS/PhD, and $22,500 for BS. Applicants must be US citizens or permanent residents capable of obtaining a SECRET or TOP SECRET clearance. Each scholar must work for the federal, state, local, or tribal government (for pay) for one year for each year of award.

Awards made for 2017-2018 will be for one year only, with the potential of renewal if funding permits (we should know by August 31, 2017).  The number of awards to be made will be determined by available funds, since there are differences in costs depending on level and in-state status (we have approximately $352,000 to award in 2017-2018).

All applications must be submitted in paper form with official transcripts and signed original letters on letterhead—no staples, folders, or binders.

Application Deadline: 12noon, Friday, July 14, 2017.   If positions remain open after the deadline, we will continue to accept applications until classes start.

See  and for more details.

UMBC Data Science Graduate Programs Start in Fall 2017


UMBC Data Science Graduate Programs

UMBC’s Data Science Master’s program prepares students from a wide range of disciplinary backgrounds for careers in data science. In the core courses, students will gain a thorough understanding of data science through classes that highlight machine learning, data analysis, data management, ethical and legal considerations, and more.

Students will develop an in-depth understanding of the basic computing principles behind data science, to include, but not limited to, data ingestion, curation and cleaning and the 4Vs of data science: Volume, Variety, Velocity, Veracity, as well as the implicit 5th V — Value. Through applying principles of data science to the analysis of problems within specific domains expressed through the program pathways, students will gain practical, real world industry relevant experience.

The MPS in Data Science is an industry-recognized credential and the program prepares students with the technical and management skills that they need to succeed in the workplace.

Why Data Science?

  • Organizations have a growing need for employees who are experts in the management and interpretation of big data;
  • Our classes are taught by industry experts who combine their professional experience with theory to provide a rigorous classroom experience; and
  • Our small classes are taught with a mix of in-person and online instruction, providing students the best of an in-classroom experience while allowing for work-school life balance.


The Data Science graduate program at UMBC is designed to respond to the growing regional and national demand for professionals with data science knowledge, skills, and abilities. Bringing together faculty from a wide range of fields who have a deep understanding of the real-world applications of data analytics, UMBC’s Data Science program prepares students for the workplace through hands-on experience, rigorous academics, and access to a robust network of knowledgeable industry professionals. UMBC’s graduate programs in Data Science offers a wide variety of benefits:

  • Exceptional faculty. The Data Science curriculum brings together UMBC’s Departments of Computer Science & Electrical Engineering, Information Systems, Mathematics and Statistics, and several departments from the social sciences to provide students with a rigorous and thorough base of knowledge. Faculty have particular strengths in addressing critical social questions through the application of data science.
  • Rigorous research. UMBC is classified by the Carnegie Foundation as a Research University (High Research Activity).
  • National recognition. For six years running (2009-2014), UMBC was ranked #1 in the U.S. News and World Report’s list of “national up-and-coming” universities
  • Convenient classes. Classes are conveniently offered in the evening on UMBC’s main campus, located just ten minutes from BWI Airport, with easy access to I-95 and the 695 Beltway

For more information and to apply online, see the Data Science MPS site.

ABC features UMBC cybersecurity student scholars

Students at UMBC are learning how to hack into systems and prevent attacks. They study hardwarre, software and the tools in between.


Jamie Costello from ABC’s Baltimore affiliate WMAR has a short video feature, UMBC is on a mission to crack the code, on UMBC students who are studying and doing research on the cybersecurity of computing hardware, software and systems.

If you walk through your door and notice your home computer in pieces scattered throughout the house, call UMBC.

In the old days, parents wanted their children to grow up to become doctors and lawyers, now its about becoming cyber security experts.

A select group of students at UMBC knew this was for them. Some tore computers apart. Some knocked XBOX players off their game on purpose. And one student, while in high school and with the school’s blessing, hacked into the school’s security camera system.

Jobs are like gnats on a summer night, college graduates are swatting the offers away. And the pay is good, really good.

Students are learning how to hack into systems and then prevent such attacks. They are studying hardware, software and tools in between. The more we invent and tie into the internet, the more cyber security experts are needed.

Virtual Reality Design for Science student projects, 12-1:30 Wed. 5/10, ITE 201b


Everyone is invited to see presentations and demonstrations of  six class projects done by the 17 students in CMSC 491/691, Virtual Reality Design for Science, taught by CSEE Professor Jian Chen this spring.  The demonstrations and presentations will take place 12:00-1:30pm Wednesday, 10 May 2017 in the π² Immersive Hybrid Reality Lab located in room 201b in the ITE building. Join us in this new adventure to explore ideas and foster interaction and interdisciplinary science. Pizza will be provided.

  • Utilizing VR simulations to study the effect of food labeling on college students meal choices, by Elsie, Kristina, and Michael
  • Integrating spatial-and-non-spatial approaches for interactive quantum physics data analyses, by Henan, John, and Nick
  • Analyzing the benefits of immersion for environmental research, by Caroline, James, and Peter
  • CPR training effectiveness, by Joey, Justin, and Zach
  • Quantitative measurement of cosmological pollution visualization, by Kyle, Pratik, and Vineet
  • Memorable mobile-VR-based campus tour, by Abhinav and Vincent

Support for this new course was provided by an award from the UMBC Hrabowski Fund for Innovation to CSEE Professors Jian Chen, Marc Olano and Adam Bargteil.  The project-oriented class introduces students to the use of hybrid reality displays, 3D modeling, visualization and fabrication to conduct and analyze scientific research. The new course embraces the university’s goal of advancing interdisciplinary and multidisciplinary research activity.

The UMBC π² Immersive Hybrid Reality Lab is funded by a $360,000 NSF award, with additional support from Next Century Corporation. In the lab, users wear 3D glasses with sensors attached to them and operate handheld controls that allow them to sensorially immerse themselves in data, which appears on dozens of high-resolution screens that are precisely aligned to work together. Users control the data by manipulating it in the space around them. The user’s body is fairly stationary, but the brain thinks the body is moving within the virtual world. The lab brings together tools “that will allow humans and the computer to augment each other,” notes Dr. Chen.

talk: Data-Driven Applications in Smart Cities, 1pm Fri May 5

UMBC CSEE Seminar Series

Data-Driven Applications in Smart Cities—Data and Energy Management in Smart Grids

Zhichuan Huang
University of Maryland, Baltimore County

1:00-2:00pm, Friday, 5 May 2017, ITE 231

The White House announced the Smart Cities Initiative with an $160 million investment to address emerging challenges in this inevitable urbanization. Under the scope of this initiative, my work addresses emerging problems in the smart energy systems in connected communities with a data-driven approach, including sensing hardware design, streaming data collection to data analytics and privacy, system modeling and control, application design and deployments. In this talk, I will focus on an example of data driven solutions for data and energy management in smart grids. I will first show how to collect the energy data from large-scale deployed low-cost smart meters and minimize the communication and storage overhead. Then I will show how we can conduct energy data analytics with the collected energy data and utilize data analytics results for real-time energy management in a microgrid to minimize the operational cost. Finally, I will present the real-world impact of my research and some future work about CPS in smart cities.


Zhichuan Huang is a Ph.D. candidate in Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County. He is interested in incorporating big data analytics in Cyber-Physical Systems (also known as Internet of Things under some contexts) for data driven applications in Smart Connected Communities. His current focus is on data driven solutions for smart energy systems including from sensing hardware design, streaming data collection to data analytics and privacy, system modeling and control, application design and deployments. His technical contributions have led to more than 20 papers, featuring 14 first-author papers in premier venues, e.g., IEEE BigData, ICCPS, IPSN, RTSS and best paper runner-up in BuildSys 2014.

Organizer: Tulay Adali

About the CSEE Seminar Series: The UMBC Department of Computer Science and Electrical Engineering presents technical talks on current significant research projects of broad interest to the Department and the research community. Each talk is free and open to the public. We welcome your feedback and suggestions for future talks.

Microsoft launches competition to create collaborative AI system to play Minecraft


A Microsoft Research team challenged PhD students to craft an advanced AI-based system that can collaborate with people in playing the popular Minecraft game, offering three $20K prizes. Minecraft was chosen because it offers an environment that, which relatively simple in some ways, it requires advances in areas that are still difficult for artificial computer agents to handle. The challenge asks questions like the following.

“How can we develop artificial intelligence that learns to make sense of complex environments? That learns from others, including humans, how to interact with the world? That learns transferable skills throughout its existence, and applies them to solve new, challenging problems?”

Microsoft’s Project Malmo addresses them by integrating deep reinforcement learning, cognitive science, and many AI ideas. The Malmo platform a sophisticated AI experimentation system built on top of Minecraft that is designed to support fundamental research in artificial intelligence.

A recentTechRepublic article, Microsoft competition asks PhD students to create advanced AI to play Minecraft, describes the competition and quotes UMBC Professor Marie desJardins on the project.

“Marie desJardins, AI professor at the University of Maryland, Baltimore County, sees Minecraft as an ‘interesting and challenging problem for AI systems, because of the fundamental complexity of the game environment, the open-ended nature of the scoring system, and the opportunity to collaborate with other game players (AIs or humans).’

But desJardins also raises concerns when it comes to these competitions. ‘Who owns the resulting intellectual property?” she asked. “Are these kinds of contests the best way for grad students to spend their time? Do these competitions foster or decrease diversity? Who ultimately profits from the contests?'”

The Malmo challenge is open to PhD students who register by April 14, 2017. After registration, teams of one to three members are given a task that consists of one or more mini-games. The goal is to develop an AI solution that learns how to work with other, randomly assigned players to achieve a high score in the game. Participants submit their solutions to GitHub by May 15, including a one-minute video that shows off the AI agent and summarizes what is interesting about their approach.


Microsoft’s Katja Hofmann discusses Project Malmo

Capital Area Women in Computing Celebration, 2/24-25

The Capital Area Women in Computing Celebration, sponsored by ACM-W, will be held at Georgetown University on Friday, February 24th and Saturday, February 25.

The celebration will bring together women at the high school, undergraduate, graduate, and professional levels to promote the recruitment, retention, and progression of women in computing fields.

The cost of student attendance is modest: $50 with shared hotel room, or $25 without hotel. Scholarships are available as well.

To get more information and to register, visit the CAPWIC 2017 Web site.

Reasons to Attend

  • Share your work and ideas with your peers and experts during the poster session, flash talk, or technical short.
  • Be inspired. Meet technical women like you and celebrate your accomplishments together.
  • Hear success stories of technical women who made it this far!
  • Broaden your skills by attending a workshop.
  • Meet recruiters from business, industry, and academia for internships, jobs, or graduate programs.
  • Find a new job or internship. Bring your resume to our career fair to apply for job and internship opportunities.
  • Did we mention that it is FUN!

Attacking and Defending the Automotive CAN Bus

MS Thesis Defense

Attacking and Defending the Automotive CAN Bus

Jackson Schmandt

12:30pm Thursday, 8 December, 2016, ITE 325b, UMBC

The scope and complexity of Automotive Computer Networks have grown drastically in the last decade. Once present only in high end vehicles, multi-use infotainment systems are now included in base models of some economy vehicles. Frequently connected to drivetrain components, these systems bring out multiple network access points, many of which are wireless. This unprecedented access has led to several high-profile exploits from both white-hat hackers and criminals. Although industry members are working toward long-term solutions, current systems suffer from inadequate protocol security and a lack of common-sense design practices. To address the security problem in the short term, this thesis describes a flexible Message Authentication Code that can be retrofitted with software only, as well as implementations on microcontrollers, an FPGA and an ASIC design. This work shows that on current embedded controllers, message authentication tags can be generated or verified in under 400 microseconds and in under 10 microseconds on a special-purpose ASIC.

Committee Members: Drs. Nilanjan Banerjee (chair), Alan Sherman (co-chair) and Anupam Joshi

PhD defense: Deep Neural Networks in Real-Time Embedded Systems


PhD Dissertation Defense

Deploying Deep Neural Networks in Real-Time Embedded Systems

Adam Page

10:00am Monday, 21 November 2016, ITE 325b

Deep neural networks have been shown to outperform prior state-of-the-art solutions that rely heavily on hand-engineered features coupled with simple classification techniques. In addition to achieving several orders of magnitude improvement, they offer a number of additional benefits such as the ability to perform end-to-end learning by performing both hierarchical feature abstraction and inference. Furthermore, their success continues to be demonstrated in a growing number of fields for a wide-range of applications, including computer vision, speech recognition, biomedical, and model forecasting. As this area of machine learning matures, a major challenge that remains is the ability to efficiently deploy such deep networks in embedded, resource-bound settings that have strict power and area budgets. While GPUs have been shown to improve throughput and energy efficiency over traditional computing paradigms, they still impose significant power burden for such low-power embedded settings. In order to further reduce power while still achieving desired throughput and accuracy, classification-efficient networks are required in addition to optimal deployment onto embedded hardware.

In this dissertation, we target both of these enterprises. For the first objective, we analyze simple, biologically-inspired reduction strategies that are applied both before and after training. The central theme of the techniques is the introduction of sparsification to help dissolve away the dense connectivity that is often found at different levels in neural networks. The sparsification techniques developed include feature compression partition, structured filter pruning and dynamic feature pruning.

In the second contribution, we propose scalable, hardware-based accelerators that enable deploying networks in such resource-bound settings by both exploiting efficient forms of parallelism inherent in convolutional layers and by exploiting the sparsification and approximation techniques proposed. In particular, we developed SPARCNet, an efficient and scalable hardware convolutional neural network accelerator, along with a corresponding resource-aware API to reduce, translate, and deploy a pre-trained network. The SPARCNet accelerator has been fully implemented in FPGA hardware and successfully employed for a number of case studies and evaluated against several existing state-of-the-art embedded platforms including NVIDIA Jetson TK1/TX1 in real-time. A full hardware demonstration with the developed API will be showcased that enables selecting between hardware platforms and state-of-the-art vision datasets while performing real-time power, throughput, and classification analysis.

Committee: Drs. Tinoosh Mohsenin (chair), Anupam Joshi, Tim Oates, Mohamed Younis, Farinaz Koushanfar

Dissertation defense: Cross-Layer Techniques for Boosting Base-Station Anonymity in Wireless Sensor Networks

Dissertation Defense Announcement

Cross-Layer Techniques for Boosting Base-Station Anonymity in Wireless Sensor Networks

Sami Alsemairi

9:30 Wednesday, 9 November 2016, ITE 346

Wireless Sensor Networks (WSNs) provide an effective solution for surveillance and data gathering applications in hostile environments where human presence is infeasible, risky or very costly. Examples of these applications include military reconnaissance, guarding boarders against human trafficking, security surveillance, etc. A WSN is typically composed of a large number of sensor nodes that probe their surrounding and transmit measurements over multi-hop paths to an in-situ Base-Station (BS). The BS not only acts as a sink of all collected sensor data but also provides network management and serves as a gateway to remote commend centers. Such an important role makes the BS a target of adversary attacks that opt to achieve Denial-of-Service (DoS) and nullify the WSN utility to the application. Even if the WSN applies conventional security mechanisms such as authentication and data encryption, the adversary may apply traffic analysis techniques to locate the BS and target it with attacks. This motivates a significant need for boosting BS anonymity to conceal its location.

In this dissertation, we address the challenges of BS anonymity and develop a library of techniques to counter the threat of traffic analysis. The focus of our work is on the link and network layers. We first exploit packet combining as a means to vary the traffic density throughout the network. We call this technique combining the data payload of multiple packets (CoDa), where a node groups the payload of multiple incoming data packets into a single packet that is forwarded toward the BS. CoDa cuts on the number of transmissions that constitute evidences for implicating the BS as a destination of all traffic and thus degrades the adversary’s ability in conducting effective traffic analysis.

Next we develop a novel technique for increasing BS anonymity by establishing a sleep/active schedule among the nodes that are far away from the BS, and increasing the traffic density in selected parts of the network in order to give the impression that the BS is located in the vicinity of the sleeping nodes. We call this technique Adaptive Sampling Rate for increased Anonymity (ASRA). Moreover, we develop three novel techniques based on a hierarchical routing topology. The first, which we call Hierarchical Anonymity-aware Routing Topology (HART), forms clusters and an inter-cluster-head routing topology so that a high traffic volume can be observed in areas away from the BS. The second is a novel cross-layer technique that forms a mesh topology. We call this technique cluster mesh topology to boost BS’s anonymity (CMBA). CMBA opts to establish a routing topology such that the traffic pattern does not implicate any particular node as a sink.

The third technique creates multiple mesh-based routing topologies among the cluster-heads (CHs). By applying the closed space-filling curves such as the Moore curve, for forming a mesh, the CHs are offered a number of choices for disseminating aggregated data to the BS through inter-CH paths. Then, the BS forwards the aggregated data as well so that it appears as one of the CH. We call this technique boosting the BS anonymity through multiple mesh-based routing topologies (BAMT). We validate the effectiveness of all anonymity-boosting techniques through simulation and highlight the trade-off between anonymity and overhead.

Committee: Drs. Mohamed Younis (Chair), Charles Nicholas, Chintan Patel, Richard Forno and Waleed Youssef

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